Dot Product demands the homophily of graph data to effectively infer the link between nodes.
In contrast, thanks to the universal approximation capability, MLP can approximate any continuous function, and thus does not demand the homophily of graph data for effective LP.
This gap in the expressiveness accounts for the performance difference of these two decoders on many datasets (see Sec. REF ).
We additionally show in Appendix.  that using a HadamardMLP is easy to learn Dot Product, which also partially accounts for the better effectiveness of the HadamardMLP decoders over the Dot Product.
Existing work also finds that the effectiveness of Bilinear and ConcatMLP is generally worse than the HardmardMLP or Dot Product decoder [1]}, [2]}, [3]}, [4]}.
We confirm these findings more rigorously in the empirical results in Fig. REF  and more complete in Sec. REF .
